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BS, MS, or PhD in Computer Science, Machine Learning, or a related technical field, or equivalent practical experience
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Hands-on experience developing and evaluating machine-learning, data-mining, computer-vision, or information-retrieval methods with a framework such as PyTorch or TensorFlow, including experimentation, error analysis, and principled metrics
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Foundations for data-centric machine learning: an understanding of model uncertainty and evaluation, embeddings and similarity search, and how training-data composition shapes model behavior
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Self-driven with a strong sense of ownership: a quick learner who is eager to take responsibility and drive projects forward end to end
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Production full-stack experience spanning backend services and REST APIs, relational data modeling and SQL, and modern JavaScript or TypeScript frontend development using React or a comparable framework
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Experience improving models through data — training or fine-tuning, active learning and data flywheels, hard-example mining, uncertainty or disagreement signals, dataset curation — ideally in autonomous driving, robotics, or perception
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Experience with embedding and multimodal models (CLIP-style models, VLMs), vector databases (Milvus, FAISS, pgvector), or GPU batch inference at scale